Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study
Nada Hegazy
1, 2, 3, 4
,
K. Ken Peng
5, 6
,
Patrick M Daoust
3, 4
,
Lakshmi Pisharody
3, 4
,
Elisabeth Mercier
1, 2, 3, 4
,
Élisabeth Mercier
3, 4
,
Nathan Thomas Ramsay
1, 2, 3, 4
,
Md. Pervez Kabir
1, 2, 3, 4
,
Tram Bich Nguyen
3, 4
,
Emma Tomalty
1, 2, 3, 4
,
Felix Addo
1, 2, 3, 4
,
Chandler Hayying Wong
1, 2, 3, 4
,
Wan Shen
1, 2, 3, 4
,
Joan Hu
5, 6, 7, 8
,
Charmaine Dean
7, 9, 10, 11
,
Charmaine B Dean
10, 11
,
Minqing Ivy Yang
12, 13, 14, 15
,
M Ivy Yang
14, 15
,
Hadi Dhiyebi
11, 16
,
Elizabeth A. Edwards
12, 13, 14, 15
,
Mark R. Servos
11, 16
,
Gustavo Ybazeta
17
,
Marc Habash
18, 19, 20, 21
,
M.B Habash
20, 21
,
Lawrence Goodridge
21, 22
,
Art F.Y. Poon
23, 24
,
Eric J Arts
24, 25
,
Stephen Brown
26, 27
,
Sarah Jane Payne
27, 28
,
Andrea Kirkwood
29, 30, 31, 32
,
Andrea E. Kirkwood
31, 32
,
Denina B D Simmons
29, 30, 31, 32
,
Jean-Paul Desaulniers
29, 30, 31, 32
,
Banu Ormeci
33, 34, 35, 36
,
Banu Örmeci
35, 36
,
Christopher Kyle
37, 38, 39, 40
,
David Bulir
41, 42, 43, 44
,
Trevor Charles
9, 11, 16, 45
,
T. C. Charles
11, 16
,
R.Michael L McKay
46, 47, 48, 49
,
K. A. Gilbride
50, 51, 52, 53
,
Kimberley A Gilbride
52, 53
,
Claire Jocelyn Oswald
51, 53, 54, 55
,
Claire Oswald
53, 55
,
Hui Peng
13, 15, 56, 57
,
Christopher DeGroot
58, 59
,
Elizabeth Renouf
1, 2, 3, 4
,
Robert Delatolla
3, 4
1
Department of Civil Engineering
2
University of Ottawa
3
Department of Civil Engineering, Ottawa, Canada
|
5
Department of Statistics and Actuarial Science, Burnaby, Canada
|
7
Department of Statistics and Actuarial Science
10
Department of Statistics and Actuarial Science, Waterloo, Canada
|
12
BioZone, Department of Chemical Engineering and Applied Chemistry
13
University of Toronto
|
14
BioZone, Department of Chemical Engineering and Applied Chemistry, Toronto, Canada
|
16
Department of Biology, Waterloo, Canada
|
17
Health Sciences North Research Institute, Sudbury, Canada
|
18
School of Environmental Sciences
20
School of Environmental Sciences, Guelph, Canada
|
22
Canadian Research Institute for Food Safety, Department of Food Science, Guelph, Canada
|
23
Department of Pathology and Laboratory Medicine, London, Canada
|
25
Department of Microbiology and Immunology, London, Canada
|
26
Department of Chemistry, Kingston, Canada
|
28
Department of Civil Engineering, Kingston, Canada
|
29
Faculty of science
30
Ontario Tech University
|
31
Faculty of Science, Oshawa, Canada
|
33
Department of Civil and Environmental Engineering
34
Carleton University
35
Department of Civil and Environmental Engineering, Ottawa, Canada
|
37
Department of Forensic Science
39
Department of Forensic Science, Peterborough, Canada
|
41
Department of Chemical Engineering
43
Department of Chemical Engineering, Hamilton, Canada
|
45
Department of Biology
46
Great Lakes Institute for Environmental Research, School of the Environment
48
Great Lakes Institute for Environmental Research, School of the Environment, Windsor, Canada
|
50
Department of Chemistry and Biology
52
Department of Chemistry and Biology, Toronto, Canada
|
54
Department of Geography and Environmental Studies
55
Department of Geography and Environmental Studies, Toronto, Canada
|
56
DEPARTMENT OF CHEMISTRY
57
Department of Chemistry, Toronto, Canada
|
58
Department of Mechanical and Materials Engineering, London, Canada
|
Publication type: Journal Article
Publication date: 2025-03-07
scimago Q1
wos Q1
SJR: 1.268
CiteScore: 7.1
Impact factor: 4.3
ISSN: 26900637
Abstract
The emergence of COVID-19 in Canada has led to over 4.9 million cases and 59,000 deaths by May 2024. Traditional clinical surveillance metrics (hospital admissions and clinical laboratory-positive cases) were complemented with wastewater and environmental monitoring (WEM) to monitor SARS-CoV-2 incidence. However, challenges in public health integration of WEM persist due to perceived limitations of WEM data quality, potentially driving inconsistent correlations variability and lead times. This study investigates how factors like population size, WEM measurement magnitude, site isolation status, hospital admissions, and clinical laboratory-positive cases affect WEM data correlations and variability in Ontario. The analysis uncovers a direct relationship between clinical surveillance data and the population size of the surveyed sewersheds, while WEM measurement magnitude was not directly impacted by population size. Higher variability in clinical surveillance data was observed in smaller sewersheds, likely reducing correlation strength for inferring COVID-19 incidence. Population size significantly influenced correlation quality, with thresholds identified at ∼66,000 inhabitants for strong WEM-hospital admissions correlations and ∼68,000 inhabitants for WEM-laboratory-positive cases during waned vaccination periods in Ontario (the Omicron BA.1 wave). During significant vaccination immunization (the Omicron BA.2 wave), these thresholds increased to ∼187,000 and 238,000, respectively. These findings highlight the benefit of WEM for strategic public health monitoring and interventions, especially in smaller communities. This study provides insights for enhancing public health decision making and disease monitoring through WEM, applicable to COVID-19 and potentially other diseases.
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Hegazy N. et al. Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study // ACS ES&T Water. 2025. Vol. 5. No. 4. pp. 1605-1619.
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Hegazy N. et al. Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study // ACS ES&T Water. 2025. Vol. 5. No. 4. pp. 1605-1619.
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@article{2025_Hegazy,
author = {Nada Hegazy and K. Ken Peng and Patrick M Daoust and Lakshmi Pisharody and Elisabeth Mercier and Élisabeth Mercier and Nathan Thomas Ramsay and Md. Pervez Kabir and Tram Bich Nguyen and Emma Tomalty and Felix Addo and Chandler Hayying Wong and Wan Shen and Joan Hu and Charmaine Dean and Charmaine B Dean and Minqing Ivy Yang and M Ivy Yang and Hadi Dhiyebi and Elizabeth A. Edwards and Mark R. Servos and Gustavo Ybazeta and Marc Habash and M.B Habash and Lawrence Goodridge and Art F.Y. Poon and Eric J Arts and Stephen Brown and Sarah Jane Payne and Andrea Kirkwood and Andrea E. Kirkwood and Denina B D Simmons and Jean-Paul Desaulniers and Banu Ormeci and Banu Örmeci and Christopher Kyle and David Bulir and Trevor Charles and T. C. Charles and R.Michael L McKay and K. A. Gilbride and Kimberley A Gilbride and Claire Jocelyn Oswald and Claire Oswald and Hui Peng and Christopher DeGroot and Elizabeth Renouf and Robert Delatolla and others},
title = {Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study},
journal = {ACS ES&T Water},
year = {2025},
volume = {5},
publisher = {American Chemical Society (ACS)},
month = {mar},
url = {https://pubs.acs.org/doi/10.1021/acsestwater.4c00958},
number = {4},
pages = {1605--1619},
doi = {10.1021/acsestwater.4c00958}
}
Cite this
MLA
Copy
Hegazy, Nada, et al. “Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study.” ACS ES&T Water, vol. 5, no. 4, Mar. 2025, pp. 1605-1619. https://pubs.acs.org/doi/10.1021/acsestwater.4c00958.
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